Computational techniques to find and suppress bone from chest radiological images.
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Data
2023
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Resumo
The proposal of this work is to propose bone suppression techniques in chest images. The most
common, but inaccessible, way is through Dual Energy Subtraction (DES). This the technique
requires specific hardware to generate and receive di
erent energy levels capable of di
erentiating
materials by atomic number. This work uses GAN to perform bone suppression on X-ray images
and aimed to evaluate the performance of the cGAN, train a model to locate the thoracic box, and
assess two di
erent training techniques for boneless image translation. Based on deep learning
the main contribution of this work is to improve the bone shadow elimination delimiting the
learning region of the Deep Learning (DL) model. By the contextualization of the bones region,
was possible present a metric that measures the model accuracy in an interested region. With
this study was possible a more precise metric to evaluate the bone suppression quality. Using
the Japanese Society of Radiological Technology (JSRT) this study achieved a PSNR index of
31.604, and a similarity coe
cient, known as SSIM of 0.9402. When delimiting the learning
region, the results were: 31.9136 for PSNR and 0.9633 for SSIM.
Descrição
Programa de Pós-Graduação em Ciência da Computação. Departamento de Ciência da Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto.
Palavras-chave
Deep learning, Bone suppression, Artificial intelligence, X-ray
Citação
ZIVIANI, Hugo Eduardo. Computational techniques to find and suppress bone from chest radiological images. 2023. 83 f. Dissertação (Mestrado em Ciência da Computação) - Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto, Ouro Preto, 2023.